Background and Aims
Detection and characterization of focal liver lesions (FLLs) is key for optimizing
treatment for patients who may have a primary hepatic cancer or metastatic disease
to the liver. This is the first study to develop an EUS-based convolutional neural
network (CNN) model for the purpose of identifying and classifying FLLs.
Methods
A prospective EUS database comprising cases of FLLs visualized and sampled via EUS
was reviewed. Relevant still images and videos of liver parenchyma and FLLs were extracted.
Patient data were then randomly distributed for the purpose of CNN model training
and testing. Once a final model was created, occlusion heatmap analysis was performed
to assess the ability of the EUS-CNN model to autonomously identify FLLs. The performance
of the EUS-CNN for differentiating benign and malignant FLLs was also analyzed.
Results
A total of 210,685 unique EUS images from 256 patients were used to train, validate,
and test the CNN model. Occlusion heatmap analyses demonstrated that the EUS-CNN model
was successful in autonomously locating FLLs in 92.0% of EUS video assets. When evaluating
any random still image extracted from videos or physician-captured images, the CNN
model was 90% sensitive and 71% specific (area under the receiver operating characteristic
[AUROC], 0.861) for classifying malignant FLLs. When evaluating full-length video
assets, the EUS-CNN model was 100% sensitive and 80% specific (AUROC, 0.904) for classifying
malignant FLLs.
Conclusions
This study demonstrated the capability of an EUS-CNN model to autonomously identify
FLLs and to accurately classify them as either malignant or benign lesions.
Graphical abstract
Graphical Abstract
Abbreviations:
AUROC (area under the receiver operating characteristic), CNN (convolutional neural network), FLL (focal liver lesion), FNB (fine-needle biopsy), PCS (physician-captured still), TUS (transabdominal ultrasound)To read this article in full you will need to make a payment
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References
- Prevalence and importance of small hepatic lesions found at CT in patients with cancer.Radiology. 1999; 210: 71-74
- Hepatic lesions deemed too small to characterize at CT: prevalence and importance in women with breast cancer.Radiology. 2005; 235: 872-878
- Characterizing indeterminate liver lesions in patients with localized pancreatic cancer at the time of diagnosis.Abdom Radiol (NY). 2018; 43: 351-363
- Outcome of indeterminate liver lesions on computed tomography in patients with colorectal cancer.Ann R Coll Surg Engl. 2018; 100: 382-387
- EUS-derived criteria for distinguishing benign from malignant metastatic solid hepatic masses.Gastrointest Endosc. 2015; 81 (e1181-7): 1188-1196
- Dermatologist-level classification of skin cancer with deep neural networks.Nature. 2017; 542: 115-118
- Three-class mammogram classification based on descriptive CNN features.Biomed Res Int. 2017; 2017: 3640901
- Prediction of polyp pathology using convolutional neural networks achieves "resect and discard" thresholds.Am J Gastroenterol. 2020; 115: 138-144
- Automated identification of diabetic retinopathy using deep learning.Ophthalmology. 2017; 124: 962-969
- Neural network analysis of EUS images to differentiate between pancreatic malignancy and pancreatitis.Gastrointest Endosc. 2001; 54: 625-629
- Neural network analysis of dynamic sequences of EUS elastography used for the differential diagnosis of chronic pancreatitis and pancreatic cancer.Gastrointest Endosc. 2008; 68: 1086-1094
- Diagnosis of focal liver lesions from ultrasound using deep learning.Diagn Interv Imaging. 2019; 100: 227-233
- Identity mappings in deep residual networks.arXiv. 2016; 160305027
- Scikit-learn: machine learning in Python.J Mach Learn. 2011; 12: 2825-2830
- Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy.Gastrointest Endosc. 2019; 89: 806-15 e801
- A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy.Gastrointest Endosc. 2019; 89: 189-194
- deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy.Gastroenterology. 2018; 155: 1069-1078.e1068
- Differentiation of pancreatic cancer and chronic pancreatitis using computer-aided diagnosis of endoscopic ultrasound (EUS) images: a diagnostic test.PLoS One. 2013; 8e63820
- Digital image analysis of EUS images accurately differentiates pancreatic cancer from chronic pancreatitis and normal tissue.Gastrointest Endosc. 2008; 67: 861-867
- Diagnosis of focal liver diseases based on deep learning technique for ultrasound images.Arab J Sci Eng. 2017; 42: 3127-3140
- Application of artificial neural networks for the classification of liver lesions by image texture parameters.Ultrasound Med Biol. 1996; 22: 1177-1181
- Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images.Phys Med Biol. 2003; 48: 3735-3753
- Automatic classification of focal lesions in ultrasound liver images using principal component analysis and neural networks.in: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon. 2007: 2134-2137
- Neural network based focal liver lesion diagnosis using ultrasound images.Comput Med Imaging Graph. 2011; 35: 315-323
- Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network.Biomed Mater Eng. 2015; 26: S1599-S1611
- Identifying medical diagnoses and treatable diseases by image-based deep learning.Cell. 2018; 172: 1122-31 e1129
- Fast and accurate view classification of echocardiograms using deep learning.NPJ Digital Med. 2018; 1: 6
- The role of EUS in diagnosis and treatment of liver disorders.Endosc Int Open. 2019; 7: E1262-E1275
Article info
Publication history
Published online: August 27, 2020
Accepted:
August 24,
2020
Received:
July 8,
2020
Footnotes
If you would like to chat with an author of this article, you may contact Dr Levy at [email protected]
DISCLOSURE: Mr Powers is an independent researcher who was compensated by Mayo Clinic grant funds to participate in artificial intelligence model research. Dr Iyer has received research funding from Exact Sciences and Pentax Medical and consulting fees from Medtronic. All other authors disclosed no financial relationships.
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© 2021 by the American Society for Gastrointestinal Endoscopy
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